AI RESEARCH
Learning Dynamic Stability Landscapes in Synchronization Networks
arXiv CS.LG
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ArXi:2605.23708v1 Announce Type: new The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network science or graph neural networks (GNNs). We propose a novel upstream task, learning stability landscapes, which provide deeper insights into synchronization behavior and from which many such scalar indices can be derived.